Category
Llama・オープンウェイトLLM（Llama 4 / Bedrock / 自前運用）の実装ガイド
オープンウェイトLLMの価値は『重みを所有して、改造し、自分の環境で動かせる』ことにあります。データ主権・微調整・原価最適化・ロックイン回避が要件の案件で、クローズドAPIにはできない選択肢になる。本クラスタは、Llama 4の仕組みから、Bedrock/Llama API/vLLMでのデプロイ、LoRA/QLoRAでのドメイン特化、API vs セルフホストの損益分岐、画像理解の構造化抽出、そしてライセンス遵守まで——型安全・冪等性・可観測性・回復性・コストを軸に、Llamaを本番で稼がせる設計を扱います。
6 articles in total
Foundational guide
Foundational guide (start here)
Llama Complete Guide: Shipping Meta's Open-Weight LLM to Production, Faithful to the Official Docs (Llama 4, Bedrock, Llama API)
An explanation of Meta's open-weight LLM 'Llama,' faithful to the official documentation (llama.com, Meta AI, Hugging Face). The mechanism of Llama 4 Scout/Maverick, implementation with the Llama API (OpenAI-compatible) and AWS Bedrock / Ollama/vLLM, type-safe structured output, the license (700M MAU, Built with Llama), and how to choose in the Muse Spark era — shown with production-operation code.
Related practical articles
- Llamaマルチモーダル生成AIAWS BedrockOCR
Llama 4 multimodal in practice: use image understanding for production-grade 'type-safe structured extraction'
Llama 4 is natively multimodal. With real code, it explains a production pipeline that drops images — invoices, receipts, business cards, drawings, screenshots — into structured data without guessing, covering AWS Bedrock Converse image input, boundary validation with Zod, a confidence gate, human review, and PII protection.
8 min read - LlamaファインチューニングLoRA生成AIAWS Bedrock
Practical Llama fine-tuning: specializing to your own data with LoRA/QLoRA and putting it into production
The strength of open weights is that 'you can fine-tune the weights on your own data.' This explains, with real code from a production-operations viewpoint, the steps to fine-tune Llama with LoRA/QLoRA — starting from the judgment of 'is it really necessary (RAG vs. FT)', through data preparation, torchtune/TRL implementation, an evaluation gate, merge & deploy, and the license naming convention.
10 min read - Llamaコスト最適化生成AIAWS BedrockFinOps
Designing Llama inference cost: deriving the break-even of API vs. self-hosting with TCO
An article that answers 'how much does running Llama in production cost?' not by feel but with TCO. It explains, with verifiable code and real numbers: the cost formula for usage-based billing (Bedrock, etc.) and self-hosting (GPU-hours × throughput), how to derive the break-even, and the cost-reduction levers of model routing, quantization, batching, idempotent caching, and spot GPUs.
9 min read - 生成AILLMLlamaオープンウェイト発注
Selecting commercial licenses for open-weight LLMs: treating Apache 2.0 / Llama / Qwen / Gemma as a 'design decision'
When you use open-weight LLMs like Llama, Qwen, and Gemma in business, is commercial use really free? It explains the pitfall that 'open-weight ≠ open-source ≠ free to use,' and the selection axes — commercial permissibility, MAU caps, attribution, derivative naming, and usage restrictions — from experience actually running quantized open models in a commercial product, together with a machine-readable license comparison table (this is not legal advice).
8 min read - LlamavLLM生成AIGPUMLOps
Self-hosting Llama in production with vLLM: a high-throughput inference-server operations log
A practical vLLM guide for running Llama in production on your own GPU. Maximize throughput with continuous batching and PagedAttention, pack it in with FP8 quantization and tensor parallelism, and serve it as an OpenAI-compatible endpoint. With real code, it covers how to build an inference platform that doesn't fall over: health checks, observability, autoscaling, graceful drain, Bedrock fallback, and network isolation.
8 min read